Overview

Dataset statistics

Number of variables11
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory126.0 KiB
Average record size in memory129.1 B

Variable types

Numeric10
Categorical1

Alerts

WTT is highly correlated with TARGET CLASSHigh correlation
EQW is highly correlated with TARGET CLASSHigh correlation
PJF is highly correlated with TARGET CLASSHigh correlation
HQE is highly correlated with TARGET CLASSHigh correlation
TARGET CLASS is highly correlated with WTT and 4 other fieldsHigh correlation
PTI is highly correlated with TARGET CLASSHigh correlation
TARGET CLASS is uniformly distributed Uniform
WTT has unique values Unique
PTI has unique values Unique
EQW has unique values Unique
SBI has unique values Unique
LQE has unique values Unique
QWG has unique values Unique
FDJ has unique values Unique
PJF has unique values Unique
HQE has unique values Unique
NXJ has unique values Unique

Reproduction

Analysis started2022-12-17 16:28:49.217295
Analysis finished2022-12-17 16:31:19.934017
Duration2 minutes and 30.72 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

WTT
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9496815136
Minimum0.1744116684
Maximum1.721779169
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2022-12-17T13:31:20.158385image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.1744116684
5-th percentile0.4808126997
Q10.742357677
median0.9404750904
Q31.163294705
95-th percentile1.422647519
Maximum1.721779169
Range1.547367501
Interquartile range (IQR)0.4209370277

Descriptive statistics

Standard deviation0.2896352517
Coefficient of variation (CV)0.3049814569
Kurtosis-0.5121417265
Mean0.9496815136
Median Absolute Deviation (MAD)0.2104888157
Skewness0.07022360441
Sum949.6815136
Variance0.083888579
MonotonicityNot monotonic
2022-12-17T13:31:20.440799image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.91391732661
 
0.1%
0.74301254531
 
0.1%
0.66264880251
 
0.1%
1.2354167471
 
0.1%
1.2136817991
 
0.1%
0.71058985121
 
0.1%
0.65234225851
 
0.1%
1.3365453331
 
0.1%
1.290027581
 
0.1%
0.6440122151
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
0.17441166841
0.1%
0.18173374131
0.1%
0.24040626461
0.1%
0.25380206881
0.1%
0.27264258291
0.1%
0.28910910021
0.1%
0.3065644971
0.1%
0.32055897421
0.1%
0.32887950511
0.1%
0.33564110921
0.1%
ValueCountFrequency (%)
1.7217791691
0.1%
1.6728167561
0.1%
1.667371231
0.1%
1.6505025081
0.1%
1.64293271
0.1%
1.6264605571
0.1%
1.6139311851
0.1%
1.6098846951
0.1%
1.6068453811
0.1%
1.6054520481
0.1%

PTI
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.114302541
Minimum0.4413981003
Maximum1.833756552
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2022-12-17T13:31:20.913826image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.4413981003
5-th percentile0.6927410833
Q10.9420706036
median1.118486147
Q31.307904307
95-th percentile1.521919468
Maximum1.833756552
Range1.392358452
Interquartile range (IQR)0.3658337037

Descriptive statistics

Standard deviation0.2570852621
Coefficient of variation (CV)0.2307140589
Kurtosis-0.4065297047
Mean1.114302541
Median Absolute Deviation (MAD)0.1836738127
Skewness-0.1012422881
Sum1114.302541
Variance0.06609283201
MonotonicityNot monotonic
2022-12-17T13:31:21.431027image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.1620727081
 
0.1%
0.92648786691
 
0.1%
1.1253487791
 
0.1%
0.99687475081
 
0.1%
1.0029357641
 
0.1%
0.72562820711
 
0.1%
1.3749809251
 
0.1%
0.97419388821
 
0.1%
0.92532510451
 
0.1%
1.1033160731
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
0.44139810031
0.1%
0.44499824811
0.1%
0.45741295151
0.1%
0.4652642441
0.1%
0.46534236061
0.1%
0.46673274221
0.1%
0.46720975171
0.1%
0.4820504261
0.1%
0.50185713381
0.1%
0.51511140261
0.1%
ValueCountFrequency (%)
1.8337565521
0.1%
1.8035241081
0.1%
1.7523389071
0.1%
1.7448682261
0.1%
1.7416340271
0.1%
1.7143275451
0.1%
1.705618711
0.1%
1.6916216491
0.1%
1.6872629771
0.1%
1.661820331
0.1%

EQW
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8341268969
Minimum0.1709236281
Maximum1.722724755
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2022-12-17T13:31:21.621070image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.1709236281
5-th percentile0.3987435258
Q10.6154512591
median0.8132641236
Q31.028340048
95-th percentile1.34386783
Maximum1.722724755
Range1.551801127
Interquartile range (IQR)0.4128887884

Descriptive statistics

Standard deviation0.2915538503
Coefficient of variation (CV)0.3495317696
Kurtosis-0.4075914236
Mean0.8341268969
Median Absolute Deviation (MAD)0.2071741209
Skewness0.2972757418
Sum834.1268969
Variance0.08500364765
MonotonicityNot monotonic
2022-12-17T13:31:21.854423image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.56794585371
 
0.1%
1.4876070661
 
0.1%
0.77281139351
 
0.1%
0.73498952711
 
0.1%
0.45082200291
 
0.1%
0.97417195211
 
0.1%
1.3452315311
 
0.1%
0.74834473161
 
0.1%
0.37546686811
 
0.1%
0.59131934821
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
0.17092362811
0.1%
0.19436573961
0.1%
0.22833287911
0.1%
0.23048100231
0.1%
0.23152430891
0.1%
0.23410031311
0.1%
0.24651899811
0.1%
0.26684697111
0.1%
0.26854134521
0.1%
0.27585573451
0.1%
ValueCountFrequency (%)
1.7227247551
0.1%
1.6768622591
0.1%
1.668382481
0.1%
1.6086697841
0.1%
1.5878041671
0.1%
1.5752484071
0.1%
1.5742759191
0.1%
1.5720745231
0.1%
1.5546189391
0.1%
1.5464737221
0.1%

SBI
Real number (ℝ≥0)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6820993715
Minimum0.04502666641
Maximum1.634884045
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2022-12-17T13:31:22.063639image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.04502666641
5-th percentile0.3233374095
Q10.5150097868
median0.6768346403
Q30.834316777
95-th percentile1.059452011
Maximum1.634884045
Range1.589857379
Interquartile range (IQR)0.3193069902

Descriptive statistics

Standard deviation0.2296450242
Coefficient of variation (CV)0.3366738539
Kurtosis0.1783451761
Mean0.6820993715
Median Absolute Deviation (MAD)0.1595191286
Skewness0.274569445
Sum682.0993715
Variance0.05273683712
MonotonicityNot monotonic
2022-12-17T13:31:22.349775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.7554638961
 
0.1%
0.68162961011
 
0.1%
0.63618895621
 
0.1%
0.40090107331
 
0.1%
0.92018020731
 
0.1%
0.74851281661
 
0.1%
0.54581137441
 
0.1%
0.60237829331
 
0.1%
0.74240226471
 
0.1%
0.85563190511
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
0.045026666411
0.1%
0.10898572341
0.1%
0.15574591461
0.1%
0.16803555951
0.1%
0.17217976871
0.1%
0.17899143081
0.1%
0.18827785951
0.1%
0.18983591921
0.1%
0.19950085851
0.1%
0.20641834951
0.1%
ValueCountFrequency (%)
1.6348840451
0.1%
1.529916551
0.1%
1.4780413751
0.1%
1.4752742241
0.1%
1.4098109231
0.1%
1.4025329451
0.1%
1.2994208341
0.1%
1.2791637691
0.1%
1.2546220161
0.1%
1.2420044041
0.1%

LQE
Real number (ℝ≥0)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.032336328
Minimum0.3153070078
Maximum1.650049589
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2022-12-17T13:31:22.948915image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.3153070078
5-th percentile0.627152476
Q10.8708551591
median1.035824471
Q31.198270028
95-th percentile1.437827032
Maximum1.650049589
Range1.334742581
Interquartile range (IQR)0.3274148684

Descriptive statistics

Standard deviation0.2434129535
Coefficient of variation (CV)0.2357884216
Kurtosis-0.2495605744
Mean1.032336328
Median Absolute Deviation (MAD)0.1643464036
Skewness-0.02598465764
Sum1032.336328
Variance0.05924986592
MonotonicityNot monotonic
2022-12-17T13:31:23.189684image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.78086157151
 
0.1%
1.0293471271
 
0.1%
0.95739809521
 
0.1%
0.88937126481
 
0.1%
0.80272990961
 
0.1%
1.2992623641
 
0.1%
1.1096591571
 
0.1%
0.90755842961
 
0.1%
1.2110054551
 
0.1%
0.73862707111
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
0.31530700781
0.1%
0.35848497451
0.1%
0.40888938951
0.1%
0.43010517751
0.1%
0.43460456311
0.1%
0.44092800451
0.1%
0.45865052451
0.1%
0.46504317211
0.1%
0.47512350281
0.1%
0.47641741761
0.1%
ValueCountFrequency (%)
1.6500495891
0.1%
1.6488750921
0.1%
1.6412024321
0.1%
1.6340419841
0.1%
1.631692991
0.1%
1.6147405651
0.1%
1.6121467051
0.1%
1.6092495621
0.1%
1.6087898351
0.1%
1.6086120041
0.1%

QWG
Real number (ℝ≥0)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.943534342
Minimum0.2623888469
Maximum1.666902352
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2022-12-17T13:31:23.484984image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.2623888469
5-th percentile0.5416434764
Q10.7610635317
median0.9415016708
Q31.123060095
95-th percentile1.380566821
Maximum1.666902352
Range1.404513505
Interquartile range (IQR)0.3619965632

Descriptive statistics

Standard deviation0.2561205966
Coefficient of variation (CV)0.2714480917
Kurtosis-0.4060584667
Mean0.943534342
Median Absolute Deviation (MAD)0.1813122866
Skewness0.06334905949
Sum943.534342
Variance0.06559776001
MonotonicityNot monotonic
2022-12-17T13:31:23.949364image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.35260772291
 
0.1%
0.73202465211
 
0.1%
1.1109731941
 
0.1%
0.72441547071
 
0.1%
0.66666764091
 
0.1%
1.2071315291
 
0.1%
0.46233836271
 
0.1%
0.85437956431
 
0.1%
0.78440871171
 
0.1%
1.2212464451
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
0.26238884691
0.1%
0.30801530171
0.1%
0.30929772091
0.1%
0.34988973491
0.1%
0.35260772291
0.1%
0.35547143351
0.1%
0.35825411111
0.1%
0.36003817521
0.1%
0.36189625291
0.1%
0.38196278711
0.1%
ValueCountFrequency (%)
1.6669023521
0.1%
1.6386594521
0.1%
1.6175974441
0.1%
1.6146257721
0.1%
1.586497381
0.1%
1.5455707741
0.1%
1.5346187091
0.1%
1.5261393231
0.1%
1.5226149771
0.1%
1.5133196151
0.1%

FDJ
Real number (ℝ≥0)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9634218685
Minimum0.2952280856
Maximum1.713342229
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2022-12-17T13:31:24.222859image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.2952280856
5-th percentile0.5590828785
Q10.7844066602
median0.9453330074
Q31.134851932
95-th percentile1.403975101
Maximum1.713342229
Range1.418114144
Interquartile range (IQR)0.3504452715

Descriptive statistics

Standard deviation0.2551180291
Coefficient of variation (CV)0.2648040671
Kurtosis-0.1679881805
Mean0.9634218685
Median Absolute Deviation (MAD)0.1715011222
Skewness0.2071411911
Sum963.4218685
Variance0.06508520879
MonotonicityNot monotonic
2022-12-17T13:31:24.431411image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.7596969141
 
0.1%
0.85871534471
 
0.1%
0.91493227871
 
0.1%
0.59483798231
 
0.1%
0.50248740031
 
0.1%
1.406045441
 
0.1%
1.1650539721
 
0.1%
1.1154834751
 
0.1%
1.1159096531
 
0.1%
0.39666065951
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
0.29522808561
0.1%
0.29631721931
0.1%
0.31324181391
0.1%
0.33751019861
0.1%
0.37453690211
0.1%
0.38680205681
0.1%
0.39273993941
0.1%
0.39666065951
0.1%
0.40161720131
0.1%
0.40279857061
0.1%
ValueCountFrequency (%)
1.7133422291
0.1%
1.6705741911
0.1%
1.6644931511
0.1%
1.6580070071
0.1%
1.6448476841
0.1%
1.6263507111
0.1%
1.6213665081
0.1%
1.6190004871
0.1%
1.6032582311
0.1%
1.597198461
0.1%

PJF
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.071960499
Minimum0.299475657
Maximum1.785419625
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2022-12-17T13:31:24.656333image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.299475657
5-th percentile0.5936087274
Q10.8663056482
median1.065500415
Q31.283155729
95-th percentile1.541567978
Maximum1.785419625
Range1.485943968
Interquartile range (IQR)0.4168500811

Descriptive statistics

Standard deviation0.2889816433
Coefficient of variation (CV)0.269582362
Kurtosis-0.5158956688
Mean1.071960499
Median Absolute Deviation (MAD)0.2108338788
Skewness-0.02348483043
Sum1071.960499
Variance0.08351039015
MonotonicityNot monotonic
2022-12-17T13:31:24.960595image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.64379756441
 
0.1%
0.90727259571
 
0.1%
1.4434932951
 
0.1%
0.67518944041
 
0.1%
1.2377509861
 
0.1%
1.3004461791
 
0.1%
1.1747277141
 
0.1%
0.88735502091
 
0.1%
1.2584673111
 
0.1%
1.2521143921
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
0.2994756571
0.1%
0.31975233831
0.1%
0.36984077111
0.1%
0.37387118711
0.1%
0.38340681381
0.1%
0.38958441951
0.1%
0.40939938391
0.1%
0.41313603511
0.1%
0.42329381221
0.1%
0.4260174641
0.1%
ValueCountFrequency (%)
1.7854196251
0.1%
1.76584161
0.1%
1.7647006261
0.1%
1.741918941
0.1%
1.7328262921
0.1%
1.7262117711
0.1%
1.712429731
0.1%
1.709214431
0.1%
1.7051755381
0.1%
1.699186831
0.1%

HQE
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.15825079
Minimum0.3651566099
Maximum1.885690085
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2022-12-17T13:31:25.182077image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.3651566099
5-th percentile0.6591256707
Q10.9343401315
median1.165556198
Q31.383173101
95-th percentile1.614934953
Maximum1.885690085
Range1.520533475
Interquartile range (IQR)0.44883297

Descriptive statistics

Standard deviation0.2937375166
Coefficient of variation (CV)0.2536044171
Kurtosis-0.6934295182
Mean1.15825079
Median Absolute Deviation (MAD)0.2244629606
Skewness-0.1246263155
Sum1158.25079
Variance0.08628172867
MonotonicityNot monotonic
2022-12-17T13:31:25.390629image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.87942209141
 
0.1%
0.70751145081
 
0.1%
0.7680982491
 
0.1%
1.3727746961
 
0.1%
1.2495057971
 
0.1%
1.1547136811
 
0.1%
0.84151266831
 
0.1%
1.1674864111
 
0.1%
1.4515384281
 
0.1%
1.3603694021
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
0.36515660991
0.1%
0.38629981811
0.1%
0.39988606521
0.1%
0.40691639341
0.1%
0.42131150461
0.1%
0.50661741121
0.1%
0.51035026071
0.1%
0.51562689341
0.1%
0.5159609491
0.1%
0.52287664451
0.1%
ValueCountFrequency (%)
1.8856900851
0.1%
1.8275860991
0.1%
1.8193046741
0.1%
1.8043616541
0.1%
1.7854400771
0.1%
1.7681342271
0.1%
1.7629236751
0.1%
1.7611049161
0.1%
1.7497597291
0.1%
1.7492911721
0.1%

NXJ
Real number (ℝ≥0)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.362724598
Minimum0.6396927474
Maximum1.893949603
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2022-12-17T13:31:25.628477image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.6396927474
5-th percentile1.009314364
Q11.222622614
median1.37536799
Q31.504831903
95-th percentile1.677427964
Maximum1.893949603
Range1.254256856
Interquartile range (IQR)0.2822092897

Descriptive statistics

Standard deviation0.2042250234
Coefficient of variation (CV)0.1498652213
Kurtosis0.0121978312
Mean1.362724598
Median Absolute Deviation (MAD)0.1426250013
Skewness-0.2380995959
Sum1362.724598
Variance0.04170786019
MonotonicityNot monotonic
2022-12-17T13:31:25.866767image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.2314094371
 
0.1%
1.6345484541
 
0.1%
1.643954581
 
0.1%
1.1364347071
 
0.1%
1.360611131
 
0.1%
1.6045745871
 
0.1%
1.1704561351
 
0.1%
1.7621089891
 
0.1%
1.2806762921
 
0.1%
1.0049532321
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
0.63969274741
0.1%
0.64149934891
0.1%
0.70332505371
0.1%
0.72582329781
0.1%
0.7410477371
0.1%
0.77263663241
0.1%
0.78533034161
0.1%
0.83294794111
0.1%
0.88040845131
0.1%
0.88311102151
0.1%
ValueCountFrequency (%)
1.8939496031
0.1%
1.8930143171
0.1%
1.8907617921
0.1%
1.8886752261
0.1%
1.8650595181
0.1%
1.8539027851
0.1%
1.8513240021
0.1%
1.8302259231
0.1%
1.8292849561
0.1%
1.8131436031
0.1%

TARGET CLASS
Categorical

HIGH CORRELATION
UNIFORM

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size15.6 KiB
1
500 
0
500 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1500
50.0%
0500
50.0%

Length

2022-12-17T13:31:26.077083image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-12-17T13:31:26.326054image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1500
50.0%
0500
50.0%

Most occurring characters

ValueCountFrequency (%)
1500
50.0%
0500
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1500
50.0%
0500
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1500
50.0%
0500
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1500
50.0%
0500
50.0%

Interactions

2022-12-17T13:30:57.872799image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:29:34.455900image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:29:39.014187image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:29:42.934611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:29:46.316409image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:29:53.999604image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:30:06.822345image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:30:19.652498image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:30:24.901474image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:30:36.812889image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:30:58.185261image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:29:35.215616image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:29:39.292989image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:29:43.209259image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:29:46.496210image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:29:57.207057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:30:08.993108image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:30:19.880102image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:30:25.403618image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:30:39.310366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:30:58.996068image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:29:35.597406image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:29:39.673210image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:29:43.478558image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:29:47.057918image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:29:58.345885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:30:09.490724image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:30:20.162074image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:30:25.687444image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:30:40.898225image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:31:03.871612image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:29:35.911726image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:29:39.973533image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:29:43.784680image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:29:47.373801image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:30:01.046488image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:30:13.608020image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:30:20.527725image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:30:25.938447image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:30:44.171529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:31:07.403691image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:29:36.880404image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:29:40.296178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:29:44.013326image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:29:47.787859image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:30:02.271501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:30:15.691061image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:30:20.939972image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:30:26.279460image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:30:44.556319image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:31:10.938363image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:29:37.294900image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:29:40.557047image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:29:44.747120image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:29:48.335035image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:30:02.467863image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:30:17.439146image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:30:21.689590image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:30:27.370372image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:30:44.940763image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:31:14.267377image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:29:37.770683image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:29:40.842992image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:29:45.169860image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:29:48.885758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:30:03.607892image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:30:17.752648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:30:22.099401image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:30:27.788030image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:30:47.408682image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:31:15.752124image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:29:37.988279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:29:42.096837image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:29:45.426853image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:29:49.180516image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:30:03.788764image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:30:18.440071image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:30:22.512049image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:30:29.819180image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:30:49.338162image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:31:16.238942image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:29:38.457470image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:29:42.432908image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:29:45.919264image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:29:49.554120image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:30:04.032668image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:30:18.890155image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:30:22.905447image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:30:30.532012image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:30:53.561588image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:31:16.664583image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:29:38.791655image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:29:42.639853image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:29:46.125532image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:29:51.363730image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:30:05.229010image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:30:19.298114image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:30:23.836958image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:30:31.508804image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-17T13:30:56.448387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-12-17T13:31:26.484912image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-12-17T13:31:27.479834image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-12-17T13:31:27.855156image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-12-17T13:31:28.124252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-12-17T13:31:28.396646image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-12-17T13:31:17.136495image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-17T13:31:19.019407image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

WTTPTIEQWSBILQEQWGFDJPJFHQENXJTARGET CLASS
00.9139171.1620730.5679460.7554640.7808620.3526080.7596970.6437980.8794221.2314091
10.6356321.0037220.5353420.8256450.9241090.6484500.6753341.0135460.6215521.4927020
20.7213601.2014930.9219900.8555951.5266290.7207811.6263511.1544830.9578771.2855970
31.2342041.3867260.6530460.8256241.1425040.8751281.4097081.3800031.5226921.1530931
41.2794910.9497500.6272800.6689761.2325370.7037271.1155960.6466911.4638121.4191671
50.8339281.5233021.1047431.0211391.1073771.0109301.2795381.2806770.5103501.5280440
60.9447051.2517611.0748850.2864730.9964400.4288600.9108050.7553051.1118001.1108420
70.8161741.0883920.8953430.2438600.9431231.0451311.1465361.3418861.2253241.4257840
80.7765511.4638120.7838250.3372780.7422151.0727560.8803001.3129511.1181651.2259220
90.7722800.5151110.8915960.9408621.4305680.8858761.2052310.5968581.5425800.9818791

Last rows

WTTPTIEQWSBILQEQWGFDJPJFHQENXJTARGET CLASS
9900.8761120.9424141.0606051.4780410.8187731.4736351.3063641.2973860.5228771.2863940
9911.1026121.0071630.5350510.6332200.7367910.8646631.0801281.2307311.1804971.6774091
9920.8096271.6027000.9909450.6499331.1188830.8998370.9191171.6088920.9786161.2756210
9930.7336871.0496360.7291940.8515121.5520150.9544500.4694260.8621351.4648021.0887591
9941.2126500.8390620.4560120.7734201.0912100.7943780.7366211.1623771.5127561.4151681
9951.0109531.0340060.8531160.6224601.0366100.5862400.7468110.3197521.1173401.3485171
9960.5755290.9557860.9418350.7928821.4142771.2695401.0559280.7131930.9586841.6634890
9971.1354700.9824620.7819050.9167380.9010310.8847380.3868020.3895840.9191911.3855041
9981.0848940.8617690.4071580.6656961.6086120.9438590.8558061.0613381.2774561.1880631
9990.8374600.9611840.4170060.7997840.9343990.4247620.7782340.9079621.2571901.3648371